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Features Importance

Spearman Correlation of Models

Summary of 4_Default_NeuralNetwork
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Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 16
- learning_rate: 0.05
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
2.9 seconds
Metric details
|
score |
threshold |
| logloss |
0.667389 |
nan |
| auc |
0.628768 |
nan |
| f1 |
0.662236 |
0.257732 |
| accuracy |
0.596849 |
0.461771 |
| precision |
0.58296 |
0.461771 |
| recall |
1 |
0.118667 |
| mcc |
0.194762 |
0.461771 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.667389 |
nan |
| auc |
0.628768 |
nan |
| f1 |
0.600809 |
0.461771 |
| accuracy |
0.596849 |
0.461771 |
| precision |
0.58296 |
0.461771 |
| recall |
0.619785 |
0.461771 |
| mcc |
0.194762 |
0.461771 |
Confusion matrix (at threshold=0.461771)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
1006 |
744 |
| Labeled as 1 |
638 |
1040 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of 3_Default_Xgboost
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Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: binary:logistic
- eta: 0.075
- max_depth: 6
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: auc
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
24.9 seconds
Metric details
|
score |
threshold |
| logloss |
0.671266 |
nan |
| auc |
0.619062 |
nan |
| f1 |
0.665819 |
0.346389 |
| accuracy |
0.588098 |
0.464941 |
| precision |
0.62963 |
0.665024 |
| recall |
1 |
0.0809205 |
| mcc |
0.179022 |
0.464344 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.671266 |
nan |
| auc |
0.619062 |
nan |
| f1 |
0.602029 |
0.464941 |
| accuracy |
0.588098 |
0.464941 |
| precision |
0.571123 |
0.464941 |
| recall |
0.636472 |
0.464941 |
| mcc |
0.178889 |
0.464941 |
Confusion matrix (at threshold=0.464941)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
948 |
802 |
| Labeled as 1 |
610 |
1068 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

SHAP Dependence plots
Dependence (Fold 1)

SHAP Decision plots
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Summary of 1_Baseline
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Baseline Classifier (Baseline)
- n_jobs: -1
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
0.2 seconds
Metric details
|
score |
threshold |
| logloss |
0.692927 |
nan |
| auc |
0.5 |
nan |
| f1 |
0.657266 |
0.440414 |
| accuracy |
0.489498 |
0.440414 |
| precision |
0.489498 |
0.440414 |
| recall |
1 |
0.440414 |
| mcc |
0 |
0.440414 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.692927 |
nan |
| auc |
0.5 |
nan |
| f1 |
0.657266 |
0.440414 |
| accuracy |
0.489498 |
0.440414 |
| precision |
0.489498 |
0.440414 |
| recall |
1 |
0.440414 |
| mcc |
0 |
0.440414 |
Confusion matrix (at threshold=0.440414)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
0 |
1750 |
| Labeled as 1 |
0 |
1678 |
Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of Ensemble
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Ensemble structure
| Model |
Weight |
| 4_Default_NeuralNetwork |
1 |
| 5_Default_RandomForest |
2 |
Metric details
|
score |
threshold |
| logloss |
0.664883 |
nan |
| auc |
0.631927 |
nan |
| f1 |
0.664653 |
0.332404 |
| accuracy |
0.600642 |
0.515354 |
| precision |
0.661448 |
0.635542 |
| recall |
1 |
0.0855096 |
| mcc |
0.200412 |
0.515354 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.664883 |
nan |
| auc |
0.631927 |
nan |
| f1 |
0.564152 |
0.515354 |
| accuracy |
0.600642 |
0.515354 |
| precision |
0.605605 |
0.515354 |
| recall |
0.52801 |
0.515354 |
| mcc |
0.200412 |
0.515354 |
Confusion matrix (at threshold=0.515354)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
1173 |
577 |
| Labeled as 1 |
792 |
886 |
Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of 2_DecisionTree
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Decision Tree
- n_jobs: -1
- criterion: gini
- max_depth: 3
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
8.9 seconds
Metric details
|
score |
threshold |
| logloss |
0.670126 |
nan |
| auc |
0.618115 |
nan |
| f1 |
0.661785 |
0.240481 |
| accuracy |
0.596266 |
0.543829 |
| precision |
0.630435 |
0.65111 |
| recall |
1 |
0.0412844 |
| mcc |
0.191693 |
0.543829 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.670126 |
nan |
| auc |
0.618115 |
nan |
| f1 |
0.555841 |
0.543829 |
| accuracy |
0.596266 |
0.543829 |
| precision |
0.602225 |
0.543829 |
| recall |
0.516091 |
0.543829 |
| mcc |
0.191693 |
0.543829 |
Confusion matrix (at threshold=0.543829)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
1178 |
572 |
| Labeled as 1 |
812 |
866 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

SHAP Dependence plots
Dependence (Fold 1)

SHAP Decision plots
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Summary of 5_Default_RandomForest
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Random Forest
- n_jobs: -1
- criterion: gini
- max_features: 0.9
- min_samples_split: 30
- max_depth: 4
- eval_metric_name: auc
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
11.9 seconds
Metric details
|
score |
threshold |
| logloss |
0.664804 |
nan |
| auc |
0.631465 |
nan |
| f1 |
0.664857 |
0.322972 |
| accuracy |
0.600058 |
0.521104 |
| precision |
0.661448 |
0.633334 |
| recall |
1 |
0.0407189 |
| mcc |
0.199213 |
0.521104 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.664804 |
nan |
| auc |
0.631465 |
nan |
| f1 |
0.56407 |
0.521104 |
| accuracy |
0.600058 |
0.521104 |
| precision |
0.604635 |
0.521104 |
| recall |
0.528605 |
0.521104 |
| mcc |
0.199213 |
0.521104 |
Confusion matrix (at threshold=0.521104)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
1170 |
580 |
| Labeled as 1 |
791 |
887 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

SHAP Dependence plots
Dependence (Fold 1)

SHAP Decision plots
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